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| Book/Dissertation / PhD Thesis | FZJ-2023-01457 |
2023
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
ISBN: 978-3-95806-683-0
Please use a persistent id in citations: http://hdl.handle.net/2128/34175 urn:nbn:de:0001-2023032109
Abstract: Renewable energy sources are a crucial cornerstone in the future energy sector to decelerate global warming and to reach the international reduction targets for CO2 emissions. However, the rising share of renewable energy sources challenges the planning and operating of energy systems in multiple regards: On the one hand, these sources are often intermittent and in order to provide a safe energy supply, energy storage technologies gain importance. On the other hand, different energy sectors are progressively coupled because the supply security can profit from the option to exchange energy among different sectors. In order to appropriately foresee these developments and derive realistic designs of cost-efficient future energy systems, spatiotemporally resolved energy system models have emerged as a powerful tool. Yet, just as the real energysystems, the models are becoming increasingly complex and the consideration of intermittent renewable energy sources require high temporal resolutions within these models. Accordingly, energy system models are either limited in the size of the regarded energy system, limited in the accuracy or computationally simply intractable. Therefore, methods were developed that strive to reduce the mathematical complexity of energy system models without sacrificing too much of the model’s accuracy. One of these methods is temporal aggregation, i.e. the reduction of the number of time steps considered by an energy system model in order to capture transient changes of its operation schedule. This thesis contributes to the research field of temporal aggregation techniques for energy system modeling by systematically comparing four fundamentallydifferent energy system models to each other, using a two-fold temporal aggregation for efficiently decreasing the computational burden of the regarded energy system models. Furthermore, it introduces novel algorithms to increase the accuracy of temporally aggregated energy system models significantly. In contrast to prior works in the literature, this thesis does not only assess the developed methods analytically, but also stochastically with a wide variety of different temporal aggregation configurations in order to take the differences of the models into account. The results reveal that the optimal temporal aggregation depends on two factors: The existence of temporally coupling constraints in the model, e.g. as introduced by storage technologies, and the types of time series used as input to the models. Moreover, the methods found in literatureare outperformed consistently and significantly by the methods proposed within this thesis. All methods were generically implemented in the Framework for Integrated Energy System Assessment (FINE) and resulted in a fundamental restructuring and extension of the time series aggregation module (tsam). The latter module is a python-based out-of-the-box solution for temporal aggregation techniques and is currently used by multiple different energy system modeling frameworks.
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